{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CBsUQPookVNaBFxlN9UOyLGVEIklgdxdH+orJW106IlydbUQOtMfx85ePm90M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\nCAcAAAAMI4QDAAAAhhHCAQAAAMMI4QAAAIBhhHAAAADAMMf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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ff58c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (285169837)>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_color_swatch():\n",
    "    class gg_stub:\n",
    "        pass\n",
    "    return (gg_stub() + scale_color_brewer()).manual_color_list\n",
    "\n",
    "swatch = get_color_swatch()\n",
    "df = pd.DataFrame({\"x\": range(len(swatch)), \"y\": 1, \"swatch\": swatch })\n",
    "ggplot(df, aes(x='x', y='y', color='swatch')) + \\\n",
    "    geom_point(size=20000) + \\\n",
    "    scale_color_identity() + \\\n",
    "    xlim(-0.4, 2.4) + \\\n",
    "    ylim(0.98, 1.02)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
